This project will develop a new approach for evaluating clustering in case-control data that accounts for residential histories. At present, few if any methods exist for modeling and analyzing residential histories using data from epidemiologic case-control studies. Local, global and focused tests for residential histories will be developed based on sets of matrices of nearest neighbor relationships that reflect the changing space-time geometry of the residential addresses of cases and controls. Exposure traces that account for the latency between exposure and disease manifestation, and that use exposure windows of varying duration will be defined. Several of the methods so derived will be applied to evaluate clustering of residential histories in an ongoing case-control study of bladder cancer in south eastern Michigan. Because humans are mobile, these new methods are a significant advance over approaches that ignore residential histories and instead rely only on place of residence at time of diagnosis or death. The major innovation is the creation of methods for analyzing and modeling the residential histories of cases and controls to identify geographic excesses of cancer risk, both in the study population itself as well as in relation to putative hazards such as point-source releases of carcinogens. The techniques and software to be developed in this project will provide a more concise and accurate description of clustering of cancer cases that accounts for residential history, cancer latency, time of diagnosis, and the exposure windows during which causative exposures are hypothesized to have occurred. [unreadable] [unreadable]